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https://doi.org/10.62836/jitp.2024.367
Alan Wilson, Kaixian Xu, Zhaoyan Zhang, & Yu Qiao. (2024). The Interpretable Artificial Neural Network in Vehicle Insurance Claim Fraud Detection Based on Shapley Additive Explanations. Journal of Information, Technology and Policy, 1–12. https://doi.org/10.62836/jitp.2024.367
Volume 2, Issue 1, 2024
Copyright (c) 2024 Alan Wilson, Kaixian Xu, Zhaoyan Zhang, Yu Qiao
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This work is licensed under a Creative Commons Attribution 4.0 International License.

The Interpretable Artificial Neural Network in Vehicle Insurance Claim Fraud Detection Based on Shapley Additive Explanations

Vehicle insurance claim fraud presents a major challenge in the insurance industry, leading to financial losses and increased premiums for policyholders. Traditional fraud detection methods, such as rule-based systems and manual claim assessment, struggle to handle the complexity and growing volume of fraudulent claims. With the advancement of Machine Learning (ML), models such as Artificial Neural Networks (ANNs) have significantly improved fraud detection accuracy. However, a key limitation of existing ML-based methods is their lack of interpretability, making it difficult for insurers to justify fraud detection decisions. To address this issue, this study proposes an interpretable fraud detection framework based on an ANN integrated with Shapley Additive Explanations (SHAP). The framework involves preprocessing insurance claim data, training an ANN for fraud prediction, and applying SHAP to analyze feature importance and provide interpretability. Experimental results demonstrate that the proposed model achieves high accuracy in fraud detection while offering insights into influential features affecting claim decisions. The findings highlight the importance of incorporating explainability into ML-based fraud detection, ensuring transparency and trustworthiness in the insurance industry.

vehicle insurance claim fraud detection; shapley additive explanations; machine learning; neural network

References

  1. Roy R, George KT. Detecting Insurance Claims Fraud Using Machine Learning Techniques. In Proceedings of the 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Kollam, India, 20–21 April 2017; pp. 1–6.
  2. Li P, Shen B, Dong W. An Anti-Fraud System for Car Insurance Claim Based on Visual Evidence. arXiv 2018; arXiv:1804.11207.
  3. Roriz R, Pereira JL. Avoiding Insurance Fraud: A Blockchain-Based Solution for the Vehicle Sector. Procedia Computer Science 2019; 164: 211–218.
  4. Viaene S, Ayuso M, Guillen M, et al. Strategies for Detecting Fraudulent Claims in the Automobile Insurance Industry. European Journal of Operational Research 2007; 176(1): 565–583.
  5. Emerson RW. Insurance Claims Fraud Problems and Remedies. UMLR 1991; 46: 907.
  6. Zhang Z. RAG for Personalized Medicine: A Framework for Integrating Patient Data and Pharmaceutical Knowledge for Treatment Recommendations. Optimizations in Applied Machine Learning 2024; 4(1).
  7. Xu K, Gan Y, Wilson A. Stacked Generalization for Robust Prediction of Trust and Private Equity on Financial Performances. Innovations in Applied Engineering and Technology 2024; 3(1): 1–12.
  8. Zhou T, Zhang G, Cai Y. Residual Self-Attention-Based Temporal Deep Model for Predicting Aircraft Engine Failure within a Specific Cycle. Optimizations in Applied Machine Learning 2023; 3(1).
  9. Huang W, Ma J. Analysis of Vehicle Fault Diagnosis Model Based on Causal Sequence-to-Sequence in Embedded Systems. Optimizations in Applied Machine Learning 2023; 3(1).
  10. Huang W, Cai Y, Zhang G. Battery Degradation Analysis through Sparse Ridge Regression. Energy & System 2024; 4(1).
  11. Ma J, Xu K, Qiao Y, et al. An Integrated Model for Social Media Toxic Comments Detection: Fusion of High-Dimensional Neural Network Representations and Multiple Traditional Machine Learning Algorithms. Journal of Computational Methods in Engineering Applications 2022; 2(1): 1–12.
  12. Ma J, Zhang Z, Xu, K, et al. Improving the Applicability of Social Media Toxic Comments Prediction Across Diverse Data Platforms Using Residual Self-Attention-Based LSTM Combined with Transfer Learning. Optimizations in Applied Machine Learning 2022; 2(1).
  13. Ma J, Chen X. Fingerprint Image Generation Based on Attention-Based Deep Generative Adversarial Networks and Its Application in Deep Siamese Matching Model Security Validation. Journal of Computational Methods in Engineering Applications 2024; 4(1): 1–13.
  14. LaValley MP. Logistic regression. Circulation 2008; 117(18): 2395–2399.
  15. Hosmer DW Jr, Lemeshow S, Sturdivant RX. Applied Logistic Regression; John Wiley & Sons: Hoboken, NJ, USA, 2013.
  16. Zhou Z, Wu J, Cao, Z, et al. On-Demand Trajectory Prediction Based on Adaptive Interaction Car Following Model with Decreasing Tolerance. In Proceedings of the 2021 International Conference on Computers and Automation (CompAuto), Virtual, 7–9 September 2021; pp. 67–72.
  17. Zhang H, Zhu D, Gan Y, et al. End-to-End Learning-Based Study on the Mamba-ECANet Model for Data Security Intrusion Detection. Journal of Information, Technology and Policy 2024; 2(1): 1–17.
  18. Zhang G, Zhou T, Cai Y. Coral-Based Domain Adaptation Algorithm for Improving the Applicability of Machine Learning Models in Detecting Motor Bearing Failures. Journal of Computational Methods in Engineering Applications 2023; 3(1): 1–17.
  19. Zhang G, Zhou T. Finite Element Model Calibration with Surrogate Model-Based Bayesian Updating: A Case Study of Motor FEM Model. Innovations in Applied Engineering and Technology 2024; 3(1): 1–13.
  20. Gan Y, Chen X. The Research on End-to-end Stock Recommendation Algorithm Based on Time-frequency Consistency. Advances in Computer and Communication 2024; 5(4).
  21. Gan Y, Ma J, Xu K. Enhanced E-Commerce Sales Forecasting Using EEMD-Integrated LSTM Deep Learning Model. Journal of Computational Methods in Engineering Applications 2023; 3(1): 1–11.
  22. Chen X, Gan Y, Xiong S. Optimization of Mobile Robot Delivery System Based on Deep Learning. Journal of Computer Science Research 2024; 6(4): 51–65.
  23. Chen X, Wang M, Zhang H. Machine Learning-Based Fault Prediction and Diagnosis of Brushless Motors. Engineering Advances 2024; 4(3).
  24. Wang Z, Zhao Y, Song C, et al. A New Interpretation on Structural Reliability Updating with Adaptive Batch Sampling-Based Subset Simulation. Structural and Multidisciplinary Optimization 2024; 67(1): 7.
  25. Ye X, Luo K, Wang H, et al. An Advanced AI-Based Lightweight Two-Stage Underwater Structural Damage Detection Model. Advanced Engineering Informatics 2024; 62: 102553.
  26. Wang X, Zhao Y, Wang Z, et al. An Ultrafast and Robust Structural Damage Identification Framework Enabled by an Optimized Extreme Learning Machine. Mechanical Systems and Signal Processing 2024; 216: 111509.
  27. Zhao Y, Dai W, Wang Z, et al. Application of Computer Simulation to Model Transient Vibration Responses of GPLs Reinforced Doubly Curved Concrete Panel under Instantaneous Heating. Materials Today Communications 2024; 38: 107949.
  28. Hao Y, Chen Z, Sun X, et al. Planning of Truck Platooning for Road–Network Capacitated Vehicle Routing Problem. arXiv 2024; arXiv:2404.13512.
  29. Gangadhar KSNVK, Kumar BA, Vivek Y, et al. Chaotic Variational Auto Encoder Based One Class Classifier for Insurance Fraud Detection. arXiv 2022; arXiv:2212.07802.
  30. Asgarian A, Saha R, Jakubovitz D, et al. AutoFraudNet: A Multimodal Network to Detect Fraud in the Auto Insurance Industry. arXiv 2023; arXiv:2301.07526.
  31. Gupta RY, Mudigonda SS, Baruah PK, et al. Markov Model with Machine Learning Integration for Fraud Detection in Health Insurance. arXiv 2021; arXiv:2102.10978.
  32. Ribeiro MT, Singh S, Guestrin C. “Why Should I Trust You?” Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining San Francisco, CA, USA, 13–17 August 2016; pp. 1135–1144.
  33. Lundberg SM, Lee SI. A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems 2017; 30.
  34. Selvaraju RR, Cogswell M, Das A, et al. Grad-Cam: Visual Explanations from Deep Networks via Gradient-Based Localization. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 22–29 October 2017; pp. 618–626.
  35. Shrikumar A, Greenside P, Kundaje A. Learning Important Features through Propagating Activation Differences. In Proceedings of the International Conference on Machine Learning, Centre, Sydney, Australia, 6–11 August 2017; pp. 3145–3153.
  36. Lundberg SM, Erion G, Chen H, et al. From Local Explanations to Global Understanding with Explainable AI for Trees. Nature Machine Intelligence 2020; 2(1): 56–67.
  37. Caruana R, Lou Y, Gehrke J, et al. Intelligible Models for Healthcare: Predicting Pneumonia Risk and Hospital 30-Day Readmission. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, 10–13 August 2015; pp. 1721–1730.
  38. Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C. DBSMOTE: Density-Based Synthetic Minority Over-Sampling Technique. Applied Intelligence 2012; 36: 664–684.
  39. Chawla NV, Bowyer KW, Hall LO, et al. SMOTE: Synthetic Minority Over-Sampling Technique. Journal of Artificial Intelligence Research 2002; 16: 321–357.
  40. Mansourifar H, Shi W. Deep Synthetic Minority Over-Sampling Technique. arXiv 2020; arXiv:2003.09788.
  41. Dai W. Evaluation and Improvement of Carrying Capacity of a Traffic System. Innovations in Applied Engineering and Technology 2022; 1(1): 1–9.
  42. Dai W. Safety Evaluation of Traffic System with Historical Data Based on Markov Process and Deep-Reinforcement Learning. Journal of Computational Methods in Engineering Applications 2021; 1(1): 1–14.
  43. Dai W. Design of Traffic Improvement Plan for Line 1 Baijiahu Station of Nanjing Metro. Innovations in Applied Engineering and Technology 2023; 10.
  44. Agatonovic-Kustrin S, Beresford R. Basic Concepts of Artificial Neural Network (ANN) Modeling and Its Application in Pharmaceutical Research. Journal of Pharmaceutical and Biomedical Analysis 2000; 22(5): 717–727.
  45. Wu W, Dandy GC, Maier HR. Protocol for Developing ANN Models and ITS application to the Assessment of the Quality of the ANN Model Development Process in Drinking Water Quality Modelling. Environmental Modelling & Software 2014; 54: 108–127.
  46. Bordt S, von Luxburg U. From Shapley Values to Generalized Additive MODELS and back. In Proceedings of the International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 25–27 April 2023; pp. 709–745.
  47. Nohara Y, Matsumoto K, Soejima H, et al. Explanation of Machine Learning Models Using Shapley Additive Explanation and Application for Real Data in Hospital. Computer Methods and Programs in Biomedicine 2022; 214: 106584.
  48. Movsessian A, Cava DG, Tcherniak D. Interpretable Machine Learning in Damage Detection Using Shapley Additive Explanations. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering 2022; 8(2): 021101.

Supporting Agencies

  1. Funding: This research was supported by the U.S. National Science Foundation under Grant No. 1563372 and by the National Natural Science Foundation of China under Grant No. 719740361.